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Cited by 102Academic integrity and plagiarismArtificial Intelligence in EducationBenford’s Law and Fraud Detection

Unmasking academic cheating behavior in the artificial intelligence era: Evidence from Vietnamese undergraduates

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Nguyen, Hung Manh, Goto, Daisaku·2024·Education and Information Technologies
DOI 10.1007/s10639-024-12495-4
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Abstract

The proliferation of artificial intelligence (AI) technology has brought both innovative opportunities and unprecedented challenges to the education sector. Although AI makes education more accessible and efficient, the intentional misuse of AI chatbots in facilitating academic cheating has become a growing concern. By using the indirect questioning technique via a list experiment to minimize social desirability bias, this research contributes to the ongoing dialog on academic integrity in the era of AI. Our findings reveal that students conceal AI-powered academic cheating behaviors when directly questioned, as the prevalence of cheaters observed via list experiments is almost threefold the prevalence of cheaters observed via the basic direct questioning approach. Interestingly, our subsample analysis shows that AI-powered academic cheating behaviors differ significantly across genders and grades, as higher-grade female students are more likely to cheat than newly enrolled female students. Conversely, male students consistently engage in academic cheating throughout all grades. Furthermore, we discuss potential reasons for the heterogeneous effects in academic cheating behavior among students such as gender disparity, academic-related pressure, and peer effects. Implications are also suggested for educational institutions to promote innovative approaches that harness the benefits of AI technologies while safeguarding academic integrity.

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  • Proctoring software utilising facial recognition and keystroke analysis aims to deter cheating in online assessments, though ethical concerns regarding privacy and surveillance remain.

    “Proctoring software utilising facial recognition and key -stroke analysis aims to deter cheating in online assessments, though ethical concerns regarding privacy and surveillance remain [ 40, 41 ].”

    From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [41]

    The Conclusions and implications recommend advanced monitoring systems to detect AI-powered academic cheating, and the Introduction notes general privacy and ethical concerns about AI applications. Neither passage describes proctoring software using facial recognition or keystroke analysis, or links privacy and surveillance concerns specifically to such software. Page numbers are unavailable in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • This form of organisational culture develops more easily in environments where educational strategies are designed to stimulate intellectual autonomy, reflective learning, and interactive communication.

    “This form of organisational culture develops more easily in environments where educational strategies are designed to stimulate intellectual autonomy, reflective learning, and interactive communication [29,31,32].”

    From Artificial Intelligence Governance in Higher Education: The Role of Knowledge-Based Strategies in Fostering Legal Awareness and Ethical Artificial Intelligence Literacy (2025) · cites this paper as [32]

    The Conclusions and implications section recommends counseling, AI-use orientation, monitoring, and adaptive assessments to safeguard academic integrity. It does not address whether organisational culture develops more easily through strategies that foster intellectual autonomy, reflective learning, and interactive communication (searched those terms and related educational strategies). (LLM, conf=0.72)

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Cited by

Sorted by citations

Authors
Nguyen, H. M., & Goto, D.
Affiliations
Hiroshima University (JP)
ORCID
Venue
Education and Information Technologies
Publisher
Springer Science and Business Media LLC
Published
2024-02-05
ISSN
  • 1360-2357 (ISSN-L)
  • 1573-7608
DOI
10.1007/s10639-024-12495-4
OpenAlex
W4391539856
Cited by
102
References
10
Citations by year
2026: 45 · 2025: 44 · 2024: 12 · 2022: 1
FWCI
32.54
Type
journal article
Language
en
Open access
Licence
Locations
Education and Information Technologies (publishedVersion, cc-by)
Funders
  • Japan Society for the Promotion of Science (22K01478)
  • Hiroshima University
Topics
  • Social Sciences › Safety Research › Academic integrity and plagiarism
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Mathematics › Statistics and Probability › Benford’s Law and Fraud Detection
Keywords
academic dishonesty, AI chatbots, academic integrity, artificial intelligence in education, list experiment, social desirability bias, gender differences, grade level differences, peer effects, academic stress

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